基于非参数核密度估计的风电功率区间预测

由于风电的高度随机性和波动性，且风电功率的预测精度仍较低，因此传统的风电功率点预测不足以描绘风电的不确定性。在风电功率点预测值的基础上，采用非参数核密度估计方法计算风电功率预测误差的概率密度，并采用三次样条插值拟合预测误差的概率分布曲线，继而得出满足一定置信概率的风电功率预测区间。结果表明，采用风电功率区间预测能提供风电功率预测曲线和该曲线的变化范围，更有利于风电的不确定性建模。

Wind Power Interval Prediction Based on Non-parametric Kernel Density Estimation

As the high randomness and fluctuation of wind power, as well as the low precision of the power prediction, the traditional prediction of wind power point is not able to describe the uncertainty of wind power. In this article, non-parametric kernel density estimation is adopted to calculate the probability density error of wind power prediction. In addition, we get a wind power prediction interval with the method of three spline interpolation which satisfies the certain confidence interval. The results show that wind power interval forecasting can provide wind power prediction curve and its variation range. Thus, it is more suitable for wind power uncertainty modeling.